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. 2016 May 4;37(9):3337–3352. doi: 10.1002/hbm.23244

Brain GABA levels across psychiatric disorders: A systematic literature review and meta‐analysis of 1H‐MRS studies

Remmelt R Schür 1,†,✉, Luc WR Draisma 1,†, Jannie P Wijnen 2, Marco P Boks 1, Martijn GJC Koevoets 1, Marian Joëls 3, Dennis W Klomp 2, René S Kahn 1, Christiaan H Vinkers 1
PMCID: PMC6867515  PMID: 27145016

Abstract

The inhibitory gamma‐aminobutyric acid (GABA) system is involved in the etiology of most psychiatric disorders, including schizophrenia, autism spectrum disorder (ASD) and major depressive disorder (MDD). It is therefore not surprising that proton magnetic resonance spectroscopy (1H‐MRS) is increasingly used to investigate in vivo brain GABA levels. However, integration of the evidence for altered in vivo GABA levels across psychiatric disorders is lacking. We therefore systematically searched the clinical 1H‐MRS literature and performed a meta‐analysis. A total of 40 studies (N = 1,591) in seven different psychiatric disorders were included in the meta‐analysis: MDD (N = 437), schizophrenia (N = 517), ASD (N = 150), bipolar disorder (N = 129), panic disorder (N = 81), posttraumatic stress disorder (PTSD) (N = 104), and attention deficit/hyperactivity disorder (ADHD) (N = 173). Brain GABA levels were lower in ASD (standardized mean difference [SMD] = −0.74, P = 0.001) and in depressed MDD patients (SMD = −0.52, P = 0.005), but not in remitted MDD patients (SMD = −0.24, P = 0.310) compared with controls. In schizophrenia this finding did not reach statistical significance (SMD = −0.23, P = 0.089). No significant differences in GABA levels were found in bipolar disorder, panic disorder, PTSD, and ADHD compared with controls. In conclusion, this meta‐analysis provided evidence for lower brain GABA levels in ASD and in depressed (but not remitted) MDD patients compared with healthy controls. Findings in schizophrenia were more equivocal. Even though future 1H‐MRS studies could greatly benefit from a longitudinal design and consensus on the preferred analytical approach, it is apparent that 1H‐MRS studies have great potential in advancing our understanding of the role of the GABA system in the pathogenesis of psychiatric disorders. Hum Brain Mapp 37:3337–3352, 2016. © 2016 Wiley Periodicals, Inc.

Keywords: 1H‐MRS, GABA, meta‐analysis, psychopathology, MDD, ASD

INTRODUCTION

There is ample evidence for involvement of the gamma‐aminobutyric acid (GABA) system in psychiatric disorders such as schizophrenia [Gonzalez‐Burgos et al., 2015; Lewis et al., 2005; Nakazawa et al., 2012], depression [Luscher et al., 2011], bipolar disorder [Brambilla et al., 2003], anxiety [Geuze et al., 2008; Kalueff and Nutt, 2007], autism [Marín, 2012], alcohol use disorder [Kumar et al., 2009], and attention deficit/hyperactivity disorder (ADHD) [Rivero et al., 2015]. A role for GABA neurotransmission across a wide spectrum of psychiatric disorders is not surprising since GABA is present at approximately one third of all synapses in the central nervous system and shapes neural network dynamics via GABAergic interneurons [Möhler, 2007]. As a result, GABA system functionality is pivotal for physiological processes that are often affected in psychiatric disorders, for example, neural plasticity, stress reactivity, sensory processing, memory formation, and attention [Mody and Pearce, 2004; Möhler, 2007; Vinkers et al., 2010].

A variety of approaches is applied to disentangle the role of the GABA system in the etiology of psychiatric disorders, e.g. involving (epi)genetics, post mortem studies and the measurement of GABA in plasma and cerebrospinal fluid [see, e.g., the review of Luscher et al., 2011]. Currently, the only methods to directly probe the GABA system in the living human brain are proton magnetic resonance spectroscopy (1H‐MRS), positron emission tomography (PET), and single photon emission computed tomography (SPECT). Of these methods, 1H‐MRS is the only one that does not require administration of radioactive tracers or drugs. Although GABA levels are relatively low in the human brain (±1 mmol/kg [Wijtenburg et al., 2015], compared with 5–15 mmol/kg for glutamate [Govindaraju et al., 2000] for example), recent advances in 1H‐MRS techniques and increased field strengths of MRI scanners have resulted in an improved GABA detection [Wijtenburg et al., 2015]. In light of the major overlapping signal for GABA with glutamine and glutamate in standard MRS sequences due to its chemical structure, it is vital to acknowledge the importance of GABA‐specific protocols reliably disentangling the GABA signal from the glutamate and glutamine signal. Moreover, editing techniques such as MEGA‐PRESS or MEGA‐sLASER [Andreychenko et al., 2012] allow for the quantification of brain GABA independent of overlapping spectral metabolites such as creatine [Mullins et al., 2014] and with reduced macromolecular contamination of the GABA signal [Arteaga De Castro et al., 2013].

These developments have resulted in a steady increase in 1H‐MRS studies examining GABA levels in psychiatric disorders ever since the first studies in 1999 [Behar et al., 1999; Sanacora et al., 1999]. However, it is currently unknown whether brain GABA levels are consistently altered across a range of psychiatric disorders. We also do not know whether GABA levels in these disorders are state‐dependent or represent a trait characteristic and whether brain GABA levels differ in developmental disorders (such as autism) from disorders with a stronger environmental component (such as MDD). In an attempt to clarify the potential relevance of brain GABA levels, we conducted a meta‐analysis of the existing 1H‐MRS GABA studies across psychiatric disorders. Moreover, to enhance the interpretation of our results and the implications for future 1H‐MRS studies, we provide a critical discussion on the challenges of GABA quantification that are associated with the use of proton magnetic resonance spectroscopy.

METHODS

Search Strategy and Selection

We conducted Pubmed and Embase searches for relevant 1H‐MRS studies comparing brain GABA levels between patients with a psychiatric disorder and healthy controls (Supporting Information Table 1, search performed August 21, 2015). Pre‐specified inclusion criteria were: (1) human in vivo 1H‐MRS studies; (2) psychiatric patients compared with healthy controls; (3) use of an editing technique or J‐resolved 1H‐MRS to measure GABA (to guarantee sufficient quality of distinct GABA signal); (4) original article; (5) article in English. Reference lists of retrieved articles were screened for additional relevant articles. Three studies per psychiatric disorder were minimally required for meta‐analysis.

Table 1.

Study characteristics: major depressive disorder, schizophrenia, and autism spectrum disorder

Study Diagnosis Region(s)b N (Pt/control) Age (SD) Female (%) Meds (%, period) Field strength (T)
MAJOR DEPRESSIVE DISORDER
Kugaya 2003 MDD OCC 11 (6/5) 34 (8) 0 0 (10 days) 2.1
Epperson 2006 MDD OCC 23 (9/14) 31 (4) 100 0 (9 mo)e 2.1
Bhagwagar 2008 MDD‐R ACC 23 (12/11)c 38 (4) 52 0 (6 mo) 3
Walter 2009 MDD ACC (R) 24 (11/13) 37 (NA) 67 0 (1 wk) 3
Hasler 2005 MDD‐R

dm/daPFC

vmPFC

 31 (16/15) 41 (12) 77 0 (3 mo)f 3
Sanacora 1999 MDD OCC 32 (14/18) 40 (10) 41 0 (2 wk)g 2.1
Bhagwagar 2007 MDD‐R OCC 33 (15/18) 40 (14) 57 0 (3 mo) 3
Shaw 2013 MDD‐R

OCC

PFC (L)

Subcortical (L)

 34 (18/16) 22 (2) 100 0 (n.s.) 3
Hasler 2007 MDD

dm/daPFC

vmPFC

 40 (20/20) 34 (12) 65 0 (1 mo) 3
Abdallah 2014 MDD OCC 40 (23/17) 43 (12) 73 0 (4 wk) 4
Gabbay 2012 MDD ACC 41 (20/21) 16 (2) 66 0 (3 mo)g 3
Price 2009 MDD

ACC

OCC

 57 (33/24) 40 (13) 48 0 (2 wk) 3
Sanacora 2004 MDD OCC 71 (33/38) 39 (11) 48 0 (2 wk)h 2.1
SCHIZOPHRENIA
Rowland Old 2013a SZ

ACC

CSO

 20 (10/10) 50 (4) 30 100 3
Rowland Young 2013a SZ

ACC

CSO

 21 (11/10) 32 (7) 33 100 3
Yoon 2010 SZ OCC 26 (13/13) 28 (9) 15 62 3
Marsman 2014 SZ

mPFC

POC

32 (13/19)

34 (15/19)

 28 (6)d 28d 100 7
Stan 2015 SZ Hippocampus (L) 34 (18/16) 39 (10) 32 61 3
Goto 2009 SZ

FL

Basal ganglia (L)

POC

 36 (18/18) 30 (11) 50 100 4
Ongur 2010 SZ or SZAD

POC

ACC

 40 (21/19) 38 (10) 35 100 3
Kelemen 2013 SZ OCC 48 (28/20) 25 (8) 34 0 (naive) 3
Kegeles 2012 SZ or SZAD

dlPFC (L)

mPFC

 54 (32/22) 32 (10) 33 50 (2 wk)i 3
Tayoshi 2010 SZ

Basal ganglia (L)

ACC

 67 (38/29) 34 (10) 44 100 3
Rowland Old 2015a SZ or SZAD mPFC 68 (31/37) 50 (6) 35 90 3
Rowland Young 2015a SZ or SZAD mPFC 69 (29/40) 26 (5) 42 93 3
AUTISM SPECTRUM DISORDER
Harada 2011 ASD

FL (L)

Basal ganglia (L)

 22 (12/10) 6 (3) NA NAj 3
Cochran 2015 5 autism, 6 Asperger's, 2 PDD‐NOS ACC 27 (13/14) 15 (2) 0 23 3
Gaetz 2014 ASD

OCC

Temp (L)

PMC (L)

18 (8/10)

24 (13/11)

32 (17/15)

 12 (3)d 21d 24d 3
Rojas 2014 9 autism, 7 Asperger's, 1 PDD‐NOS Temp (L) 34 (17/17) 13 (5) 35 29 3
Brix 2015 ASD ACC (L) 35 (14/21) 10 (2) 0 33 3
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MDD(‐R), major depressive disorder (in remission); SZ, schizophrenia; SZAD, schizoaffective disorder; ASD, autism spectrum disorder; PDD‐NOS, pervasive developmental disorder, not otherwise specified; OCC, occipital cortex; ACC, anterior cingulate cortex; R, right; dm/daPFC, dorsomedial/dorsal anterolateral prefrontal cortex (region partly overlaps with vmPFC in the same study); (vm)PFC, (Ventromedial) prefrontal cortex; CSO, centrum semiovale; mPFC, medial prefrontal cortex; POC, parieto‐occipital cortex; L, left; FL, frontal lobe; dlPFC, dorsolateral prefrontal cortex; Temp, temporal lobe; PMC, primary motor cortex; Pt, patients; HC, healthy controls; SD, standard deviation; NA, not available; Meds, psychoactive medication use; mo, months; wk, weeks n.s., not specified; T, Tesla.

a

Rowland 2013; 2015 are two studies, both distinguishing a young and an old sample. Conform the original articles we kept this distinction in our analyses.

b

Region is midline unless otherwise specified.

c

All participants from the study of 2007 by the same group.

d

Mean of total sample, differs per region.

e

1 patient used lorazepam >2 weeks before imaging.

f

No antidepressants, other psychotropic medication not mentioned.

g

1 patient used lorazepam, 1 patient used thioridazine hydrochloride, 1 control and 1 patient received hormone replacement therapy.

h

Diphenhydramine hydrochloride use was accepted for insomnia.

i

Separate analysis with medication‐free patients.

j

10 autistic patients and 9 normal controls were sedated with triclofos sodium 20 min before imaging.

The initial search yielded 504 studies. All articles were screened on title and abstract. If uncertainty about aptness for inclusion remained, the full text article was read. This resulted in 49 relevant 1H‐MRS GABA studies (see Supporting Information Fig. S1 for a diagram according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta‐Analysis) [Moher et al., 2009]). Screening of reference lists yielded two additional articles. The pre‐specified minimum of three published studies was met for major depressive disorder (MDD) (N = 13) [Abdallah et al., 2014; Bhagwagar et al., 2007, 2008; Epperson et al., 2006; Gabbay et al., 2012; Hasler et al., 2005, 2007; Kugaya et al., 2003; Price et al., 2009; Sanacora et al., 1999, 2004; Shaw et al., 2013; Walter et al., 2009], schizophrenia (N = 10) [Goto et al., 2009; Kegeles et al., 2012; Kelemen et al., 2013; Marsman et al., 2014; Öngür et al., 2010; Rowland et al., 2013, 2015; Stan et al., 2015; Tayoshi et al., 2010; Yoon et al., 2010], autism spectrum disorder (ASD) (N = 5) [Brix et al., 2015; Cochran et al., 2015; Gaetz et al., 2014; Harada et al., 2011; Rojas et al., 2014], bipolar disorder (N = 4) [Bhagwagar et al., 2007; Brady et al., 2013; Kaufman et al., 2009; Wang et al., 2006], panic disorder (N = 3) [Goddard et al., 2001; Hasler et al., 2009; Long et al., 2013], posttraumatic stress disorder (PTSD) (N = 3) [Michels et al., 2014; Pennington et al., 2014; Rosso et al., 2014], and ADHD (N = 3) [Bollmann et al., 2015; Edden et al., 2012; Ende et al., 2015]. Less than three 1H‐MRS studies were available for alcohol dependence [Behar et al., 1999; Mason et al., 2006], premenstrual dysphoric disorder [Epperson et al., 2002; Liu et al., 2015], primary insomnia [Plante et al., 2012; Winkelman et al., 2008], borderline personality disorder [Ende et al., 2015], cocaine dependence [Ke et al., 2004], obsessive‐compulsive disorder [Simpson et al., 2012], nicotine dependence [Epperson et al., 2005], social anxiety disorder [Pollack et al., 2008], and Tourette syndrome [Tinaz et al., 2014].

Data Extraction

The following study characteristics were extracted:

  • Mean and standard deviations of GABA levels, selected brain region(s) and sample size. If means and/or standard deviations were not reported [Bhagwagar et al., 2008; Gaetz et al., 2014; Long et al., 2013; Rojas et al., 2014; Rowland et al., 2015; Yoon et al., 2010], freely available software was used (Window Ruler) to calculate these measures from the provided graphs. To ensure validity of this type of measurement, five studies were randomly chosen to calculate the correlation between factual and graphically acquired GABA levels, yielding a rho of 0.9998 (Supporting Information Fig. S2).

  • Clinical characteristics (i.e., gender, age, diagnosis, instruments used for diagnosis, use of psychotropic medication).

  • 1H‐MRS methodology details, including: magnetic field strength, voxel size, specific editing technique or J‐resolved 1H‐MRS, GABA quantification using water or creatine as a reference, tissue composition correction and software used for metabolite quantification.

Statistical Analysis

If data were available, patients with symptoms and remitted patients were separately compared with healthy individuals where appropriate. For the primary analyses, GABA levels across multiple brain areas in the same individuals were interpreted to be independent, assuming that they are not homogenously distributed or comparably altered across brain regions. This approach is analogous to Aoki et al. [2012] and has the advantage that more data can be taken into account. An important disadvantage is that there is no correction for the fact that GABA data from different brain regions in the same individual may not be independent. Therefore, in secondary analyses, we calculated the weighted average and standard deviation of GABA levels across multiple brain regions in the same individuals analogous to Luykx et al. [2012a]. Moreover, we carried out analyses separately for frontal and occipital GABA levels in disorders for which sufficient studies were available (schizophrenia and MDD). Standardized mean differences (SMD) were calculated to compare effect sizes found in different studies. Heterogeneity was evaluated using Cochrane's Q‐test and the I 2 statistic [Higgins et al., 2003]. Funnel plots were constructed and Egger's test was used to establish possible publication bias [Egger et al., 1997]. All analyses were carried out using the Comprehensive Meta‐Analysis [Borenstein et al., 2005] software developed by Biostat. A random effects model was chosen since clinical and methodological heterogeneity was assumed to be present across studies. Moreover, we assumed a common/comparable among‐study variance component across subgroups (based on region or disorder state) and combined subgroups using a random effects model.

RESULTS

Study Characteristics

General

General study characteristics are shown in Table 1 for MDD, schizophrenia and ASD and in Table 2 for bipolar disorder, panic disorder, PTSD and ADHD. Additional information on diagnostic assessments and details of the applied 1H‐MRS methodology are included in the supplemental material (Supporting Information Tables S2 and S3).

Table 2.

Study characteristics: bipolar disorder, panic disorder, PTSD, and ADHD

Study Diagnosis Region(s)a N (Pt/control) Age (SD) Female (%) Meds (%, period) Field strength (T)
BIPOLAR DISORDER
Wang 2006

5 BD‐I, 9 BD‐II, 1 BD‐NOS (Eu: 8, D: 7)

9 BD‐I, 7 BD‐II (Eu: 10, D: 3, I/H: 3)

mPFC

OCC

21 (15/6)b

22 (16/6)b

37 (14)

34 (12)

48

64

40

75

 3
Kaufman 2009 BD (Eu: 10, D: 2, M: 1)

Basal ganglia

Whole brain

 24 (13/11) 41 (13) 38 100 4
Brady 2013 BD‐I (Eu: all)

ACC

POC

 28 (14/14) 35 (12) 36 86 4
Bhagwagar 2007 BD‐I (Eu: all) OCC 34 (16/18) 37 (14) 56 0 (3 mo) 3
PANIC DISORDER
Long 2013 PD

mPFC

OCC

 19 (11/8) 39 (12) 47 0 (4 wk) 3
Goddard 2001 PD OCC 28 (14/14) 36 (8) 57 0 (1 wk) 2.1
Hasler 2009 PD

dm/daPFC

vmPFC

 34 (17/17) 35 (11) 69 0 (3 mo) 3
PTSD
Rosso 2014 PTSD

ACC

Insula (R)

 26 (13/13) 33 (12) 46 8 4
Michels 2014 PTSD

ACC

dlPFC (L)

 29 (12/17c) 40 (13) 93 33 3
Pennington 2014 PTSD

Temp

ACC

POC (R)

40 (28/12)d

43 (31/12)d

49 (33/16)d

36 (11)e 0 0 (2 wk) 4
ADHD
Edden 2012 ADHD: 10 C, 3 IA PMC (L) 32 (13/19) 10 (NAf) 28 0 (1 day) 3
Bollmann Children 2015 ADHD Basal ganglia (L) 35 (16/19) 11 (2) 43 0 (3 days) 3
Ende 2015 ADHD ACC 52 (22/30) 29 (7) 100 0 (2 wk) 3
Bollmann Adults 2015 ADHD

Basal ganglia (L)

dlPFC (L)

 54 (16/38) 34 (10) 50 0 (3 days) 3
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BD(‐I/‐II/‐NOS), bipolar disorder type 1/type 2/not otherwise specified; Eu, euthymic; D, depressed; I/H, irritable/hypomanic; PD, panic disorder; M, manic; PTSD, posttraumatic stress disorder; ADHD, attention deficit/hyperactivity disorder; C, combined type; IA, predominantly inattentive type; (v)mPFC, (ventro)medial prefrontal cortex; OCC, occipital cortex; ACC, anterior cingulate cortex; POC, parieto‐occipital cortex; dm/daPFC, dorsomedial/dorsal anterolateral prefrontal cortex (region partly overlaps with vmPFC in the same study); R, right; dlPFC, dorsolateral prefrontal cortex; L, left; Temp, temporal cortex; PMC, primary motor cortex; Pt, patients; HC, healthy controls; SD, standard deviation; NA, not available; Meds, psychoactive medication use; mo, months; wk, weeks; T, Tesla.

a

Region is midline unless otherwise specified.

b

No overlap between the two samples.

c

Healthy controls were trauma‐exposed.

d

Also PTSD patients with comorbid alcohol abuse disorder.

e

Mean of total sample, differs per region.

f

Range 8.4–12.8 years old.

For MDD, nine studies investigated depressed patients and four studies examined remitted MDD patients (Table 1). For bipolar disorder, euthymic bipolar 1 disorder patients were generally included (Table 2).

Medication use

All MDD and panic disorder studies required patients to be medication‐free for at least 1 week (range: 1 week to 9 months). GABA data of unmedicated schizophrenia patients were only available in two studies [Kegeles et al., 2012; Kelemen et al., 2013] and no formal meta‐analysis was carried out. Around 25% of ASD patients used medication. In one study, the majority of subjects were sedated with triclofos sodium prior to the 1H‐MRS measurements [Harada et al., 2011]. The percentage of medicated bipolar disorder and PTSD patients per study ranged from zero [Bhagwagar et al., 2007; Pennington et al., 2014] to a hundred [Kaufman et al., 2009]. All ADHD patients were off medication for at least 1 day.

Diagnostic criteria

Three out of 10 1H‐MRS studies in schizophrenia also included patients with schizoaffective disorder (Table 1). Three out of five studies on ASD did not specify the Diagnostic and Statistical Manual of Mental Disorders (DSM) based diagnosis, while the other two included autism, Asperger's syndrome and pervasive developmental disorder not otherwise specified. The ADHD subtype (inattentive/hyperactive/combined) was only specified in one of the three 1H‐MRS studies.

1H‐MRS methodology

Methodological 1H‐MRS parameters varied widely across studies (Supporting Information Tables S2 and S3). In three studies from the same group, the two regions of interest partially overlapped but were treated as independent outcomes for this meta‐analysis [Hasler et al., 2005, 2007, 2009]. Voxel size ranged from 9 to 75 cm3 and MRI field strength varied from 2.1T to 7T. With regard to the editing technique, 21 studies used MEGA‐PRESS and 18 studies used an in‐house editing technique (J‐editing, JPRESS, MEGA‐sLASER or J‐resolved MRS). Creatine was used as a reference compound in 26 studies, water (H2O) in 13 studies and 1 study reported values for both [Brix et al., 2015]. Eight studies adjusted GABA levels for voxel tissue composition (gray/white matter and cerebrospinal fluid), 18 studies explored differences in tissue composition between groups and included gray or white matter proportion as a covariate in case of a significant difference, while 14 studies did not correct for or did not mention tissue composition correction. Software used for the quantification of GABA and other metabolites was LCModel in 16 studies, other generally available software in 11 studies (including Gannet, (j)MRUI, SAGE, MPFIT, and ProFit) and customized in‐house software in 12 studies.

Brain GABA Levels Across Psychiatric Disorders

MDD

MDD patients exhibited significantly lower GABA levels compared with healthy controls (SMD = −0.41, 95% confidence interval [CI]: −0.70 to −0.13, P = 0.005) (Fig. 1A). Separate analyses of depressed and remitted MDD individuals demonstrated that this was the result of significantly lower GABA levels in depressed (SMD = −0.52, 95% CI: −0.89 to −0.16, P = 0.005), but not in remitted MDD patients (SMD = −0.24, 95% CI: −0.70 to 0.22, P = 0.31) (Fig. 1A). Exclusion of the earliest 1H‐MRS study with the largest SMD (−2.24) [Sanacora et al., 1999] did not alter these results in depressed MDD patients (SMD = −0.39, 95% CI: −0.71 to −0.06, P = 0.019). GABA differences between depressed MDD patients and controls were larger in occipital (SMD = −0.60, 95% CI: −1.18 to −0.02, P = 0.043) than in prefrontal regions (SMD = −0.45, 95% CI: −1.05 to 0.16, P = 0.149) (Fig. 3; Supporting Information Fig. S3). Averaging GABA levels across multiple brain regions from the same study yielded similar results for the total sample (SMD = −0.45, 95% CI: −0.79 to −0.10, P = 0.012), for depressed patients (SMD = −0.52, 95% CI: −0.93 to −0.11, P = 0.014), and for remitted MDD patients (SMD = −0.27, 95% CI: −0.92 to 0.38, P = 0.416) (Supporting Information Fig. S4A).

Figure 1.

Figure 1

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Forest plots of brain GABA levels in major depressive disorder, schizophrenia, and autism spectrum disorder. Diamond shaped orange symbols represent (from top to bottom) current MDD, remitted MDD, frontal regions in schizophrenia and non‐frontal regions in schizophrenia. Size of the blue squares is proportionate to the sample size used. OCC, occipital cortex; ACC, anterior cingulate cortex; dm/daPFC, dorsomedial dorsal anterolateral prefrontal (region partly overlaps with vmPFC in the same study); (vm/m)PFC, (Ventromedial/Medial) prefrontal cortex; MDD(‐R), major depressive disorder (remitted). FL, frontal lobe; dlPFC, dorsolateral prefrontal cortex; CSO, centrum semiovale; POC, parieto‐occipital cortex; Temp, temporal lobe; PMC, primary motor cortex. [Color figure can be viewed in the online issue, which is available at http://wileyonlinelibrary.com.]

Figure 3.

Figure 3

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Schematic overview of SMDs in GABA per region of interest for MDD, schizophrenia and ASD (minimum of two studies per region). Frontal, temporal, parietal, occipital, and basal ganglia are color coded and the brain is shown from a lateral and medial perspective. Dark blue: SMD close to 0, light blue: SMD close to −1.5, gray: not reported. Red outline: significant difference between patients and controls (P < 0.05). The following SMDs were found in this meta‐analysis and used in this figure: MDD: occipital −0.597 (P = 0.043), frontal −0.445 (P = 0.149); schizophrenia: occipital −0.343 (P = 0.232), frontal −0.197 (P = 0.313), basal ganglia −0.483 (P = 0.259); ASD: frontal −0.831 (P = 0.001), temporal −1.252 (P = 0.001). [Color figure can be viewed in the online issue, which is available at http://wileyonlinelibrary.com.]

Schizophrenia

No statistically significant differences in GABA levels were found between schizophrenia patients and healthy individuals, even though GABA levels tended to be lower in schizophrenia patients (SMD = −0.23, 95% CI: −0.48 to 0.04, P = 0.089) (Fig. 1B). This trend level effect became statistically significant after averaging GABA levels across multiple brain regions from the same study (SMD = −0.29, 95% CI: −0.56 to −0.01, P = 0.039) (Supporting Information Fig. S4B). Exclusion of one study with the largest SMD (−2.67) [Yoon et al., 2010] rendered these results non‐significant (SMD = −0.18, 95% CI: −0.37 to 0.02, P = 0.078). A subanalysis in studies measuring GABA levels in frontal regions (medial [Kegeles et al., 2012; Marsman et al., 2014] and dorsolateral prefrontal cortex [Chen et al., 2014; Kegeles et al., 2012] and an unspecified region in the frontal lobe [Goto et al., 2009]) did not show significant differences (SMD = −0.20, 95% CI: −0.56 to 0.16, P = 0.287) (Fig. 3).

ASD

Patients with ASD showed significantly lower GABA levels compared with healthy controls (SMD = −0.74, 95% CI: −1.17 to −0.31, P = 0.001) (Fig. 1C). Averaging GABA levels across multiple brain regions from the same study yielded comparable results (SMD = −0.67, 95% CI: −0.94 to −0.39, P = 2.4 × 10 − 6) (Supporting Information Fig. S4C). The largest SMD (−1.25) was found in the two studies examining the temporal lobe (see Fig. 3 for a schematic overview of regional findings in ASD as well as in MDD and schizophrenia).

Bipolar disorder, panic disorder, PTSD, and ADHD

No significant differences in GABA levels were found for bipolar disorder (SMD = 0.23, 95% CI: −0.23 to 0.69, P = 0.326), panic disorder (SMD = −0.34, 95% CI: −0.90 to 0.22, P = 0.230), PTSD (SMD = 0.02, 95% CI: −0.56 to 0.59, P = 0.948), and ADHD (SMD=−0.22, 95% CI: −0.80 to 0.37, P = 0.472) compared with controls (Fig. 2). Averaging GABA levels across multiple brain regions from the same study did not change these non‐significant differences in GABA levels (bipolar: SMD = 0.099, 95% CI: −0.48 to 0.68, P = 0.734; panic disorder: SMD=−0.46, 95% CI: −1.14 to 0.23, P = 0.192; PTSD: SMD = 0.05, 95% CI: −0.76 to 0.86, P = 0.909; ADHD: SMD = −0.41, 95% CI: −1.00 to 0.18, P = 0.176) (Supporting Information Fig. S5).

Figure 2.

Figure 2

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Forest plot showing brain GABA levels in bipolar disorder, panic disorder, PTSD, and ADHD. Size of the blue squares is proportionate to the sample size used. (v)mPFC, (ventro)medial prefrontal cortex; OCC, occipital cortex; ACC, anterior cingulate cortex; POC, parieto‐occipital cortex; dm/daPFC, dorsomedial/dorsal anterolateral prefrontal cortex (region partly overlaps with vmPFC in the same study); dlPFC, dorsolateral prefrontal cortex; Temp, temporal cortex; PMC, primary motor cortex. [Color figure can be viewed in the online issue, which is available at http://wileyonlinelibrary.com.]

Age

SMD size did not significantly depend on age in the meta‐analyses of at least five studies per diagnosis (MDD, schizophrenia and ASD; data not shown), even though the two studies examining GABA levels in a relatively older (∼50 years old) and a relatively younger sample (∼30 years old) [Rowland et al., 2013, 2015] only found significantly lower brain GABA levels in older schizophrenia patients compared with controls, even after adjusting for duration of the disorder [Rowland et al., 2015].

Publication bias

Funnel plots and Egger's tests showed no apparent publication bias and SMDs were more or less symmetrically distributed around the mean with greater dispersion of SMDs in studies that had higher standard errors (Supporting Information Figs. S6 and S7).

Heterogeneity

Significant heterogeneity was found for studies on MDD (P < 0.001, I2 = 68%), on current MDD (P < 0.001, I2 = 74%), but not on remitted MDD (P = 0.07, I2 = 49%). Exclusion of the study with the largest SMD [Sanacora et al., 1999] reduced heterogeneity but it remained significant (P = 0.003; I2 = 55%). A comparably large heterogeneity was found for schizophrenia studies (P < 0.001, I2 = 67%). Again, exclusion of the study with the largest SMD reduced heterogeneity, although it remained significant (P = 0.006, I2 = 51%) [Yoon et al., 2010]. Significant heterogeneity was found also for ASD (P = 0.026, I2 = 56%), bipolar disorder (P = 0.036, I2 = 55%), panic disorder (P = 0.043, I2 = 59%); PTSD (P < 0.001, I2 = 77%), and ADHD (P = 0.002, I2 = 77%). Heterogeneity remained significant after averaging GABA levels across multiple brain regions from the same study (data not shown), except for ASD (P = 0.775, I2 = 0%). Collectively, these findings indicate that heterogeneity may have influenced the results for all psychiatric disorders in this meta‐analysis.

DISCUSSION

The present study investigated whether brain GABA levels measured with 1H‐MRS are consistently altered across psychiatric disorders. Compared with healthy individuals, GABA levels were lower in depressed but not in remitted MDD patients. In addition, GABA levels were significantly lower in ASD patients compared with controls. For schizophrenia, the results were more equivocal: GABA levels were only significantly lower after averaging GABA levels across multiple brain regions from the same study. No significant differences in brain GABA levels were found in bipolar disorder, panic disorder, PTSD, and ADHD.

GABA in MDD

Our finding that brain GABA levels are lower in depressed MDD patient is in line with several studies showing that GABA deficits play a role in the etiology of MDD [Kalueff and Nutt, 2007; Luscher et al., 2011]. Compared with healthy individuals, there is evidence for lower GABA levels in plasma [Petty et al., 1992, 1995; Petty and Sherman, 1984] and cerebrospinal fluid [Gerner et al., 1984; Kasa et al., 1982], as well as a loss of GABAergic interneurons [Rajkowska et al., 2007] in MDD patients. We found that low brain GABA levels in MDD were state‐dependent, as there was no difference between remitted MDD patients and controls. Supporting state‐dependent GABA changes in MDD, longitudinal 1H‐MRS studies have shown normalization of brain GABA levels in MDD patients after electroconvulsive [Sanacora et al., 2003], cognitive behavioral therapy [Sanacora et al., 2006] and treatment with selective serotonin reuptake inhibitors[Sanacora et al., 2002]. Of note, some studies suggest that GABA levels in melancholic [Sanacora et al., 2004] and in treatment‐resistant MDD patients [Price et al., 2009] are lower compared with atypical or non‐treatment resistant MDD, respectively. Due to a lack of individual data, we could not distinguish between MDD subtypes in this meta‐analysis. Nevertheless, these findings underscore the potential utility of in vivo GABA levels for a diagnostic subdivision of MDD patients.

GABA in ASD

The finding that GABA levels were consistently lower in ASD fits a growing body of evidence that point to increased excitatory and reduced inhibitory neurotransmission in ASD [Hussman, 2001]. Both SPECT and PET studies have demonstrated a decrease in GABAA receptors in the frontal cortex [Mori et al., 2012] and of GABAA receptor alpha5 subunits in the nucleus accumbens and the amygdala in ASD patients [Mendez et al., 2013]. This evidence is further supported by postmortem studies that have shown decreased GABAA and GABAB receptor subunits in the superior frontal cortex [Fatemi et al., 2014] and reduced GAD65/67 levels in ASD [Fatemi et al., 2002; Yip et al., 2009]. However, evidence supporting the benefit of GABAergic drugs in ASD is limited and inconclusive [Brondino et al., 2015] and paradoxical response to treatment with conventional GABAergic agents has also been reported in ASD [Bruining et al., 2015]. Lower brain GABA levels in ASD could be the result of a loss of GABAergic interneurons [Barnes et al., 2015]. Alternatively, reduced GABA levels may be secondary and compensatory for the paradoxical excitatory effects of GABA described in some ASD patients [Bruining et al., 2015]. In contrast to the decreased central GABAergic transmission, most studies report higher peripheral GABA levels of ASD patients compared with controls [Dhossche et al., 2002; El‐Ansary et al., 2011; Russo, 2013], although conflicting evidence exists [Rolf et al., 1993]. A plausible explanation for this discrepancy of GABA findings in ASD is currently lacking, but underscores the relevance of measuring GABA indices in the brain.

GABA in Schizophrenia

Notwithstanding previous evidence that GABA system functionality is associated with schizophrenia [Gonzalez‐Burgos et al., 2015; Lewis et al., 2005; Nakazawa et al., 2012] and the relatively large number of 1H‐MRS GABA studies, GABA levels were only significantly lower compared with controls after averaging GABA levels across multiple brain regions from the same study. In light of these equivocal findings, it is important to note that use of antipsychotics may have played a role. Only two studies included medication‐free patients [Kegeles et al., 2012; Kelemen et al., 2013], despite the fact that GABAergic transmission may be most prominently impaired in antipsychotic‐naïve patients [Frankle et al., 2015]. Other sources of clinical heterogeneity may have contributed to the inconclusive evidence such as a wide range of age (25–50 years old), gender (15%–50% female subjects), and duration of illness, which was reported in only five studies and varied from 5.6 months [Rowland et al., 2015] to 25.5 years [Rowland et al., 2013].

GABA in Bipolar Disorder, Panic Disorder, PTSD, and ADHD

Although there is some evidence for altered GABA system functionality in bipolar disorder [Brambilla et al., 2003], panic disorder [Kalueff and Nutt, 2007], PTSD [Geuze et al., 2008], and ADHD [Rivero et al., 2015], our meta‐analysis did not show significant differences in brain GABA levels between these patients and healthy controls. The limited number of published 1H‐MRS studies may partially account for these results.

Interpretation of the GABA MRS Signal

For a correct interpretation of this meta‐analysis, it is important to understand the background of the 1H‐MRS GABA signal. First, the signal originates from both intra‐ and extracellular GABA, even though the majority probably comes from within GABAergic interneurons [Petroff, 2002]. With regard to the biological significance of 1H‐MRS GABA signal, two not mutually exclusive hypotheses have been proposed [Hasler et al., 2007]: (1) lower GABA signal is indicative of a loss of GABAergic interneurons; (2) lower GABA signal quantifies GABAergic inhibition since intracellular GABA levels regulate extracellular GABA levels [Jackson et al., 2000]. If the 1H‐MRS GABA signal indeed reflects GABAergic inhibition, it is likely that GABA levels are dynamic and responsive to environmental challenges. In support, GABA levels are decreased in response to psychological stress [Hasler et al., 2010] and changes in GABA levels have been reported after gabapentin administration [Cai et al., 2012]. Nevertheless, the 1H‐MRS GABA signal in healthy individuals has been reported to be relatively stable; within‐session coefficients of variance (CV) range from 7% to 13% [Bogner et al., 2010; Near et al., 2013; O'Gorman et al., 2011], which is more or less consistent with CVs of measurements carried out up to 7 months apart (3.5%–21%) [Evans et al., 2010; Near et al., 2014; Stephenson et al., 2011; Wijtenburg et al., 2013]. Overall, we do not know to what degree the GABA signal varies as a result of normal physiological variation and whether absolute GABA levels and variability are specific for certain brain regions.

Methodological MRS Considerations

In addition to the interpretation of the 1H‐MRS GABA signal, there are several methodological issues that need to be considered. First, GABA levels probably differ across brain regions. For example, GABA levels differed two‐fold between brain regions measured in the same study [Kegeles et al., 2012; Tayoshi et al., 2010]. Therefore, a hypothesis‐driven regional approach is essential as long as whole‐brain approaches with sufficiently high spatial resolution of the 1H‐MRS signal are absent. Many studies have focused on the occipital cortex since the spectral resolution is higher compared with most other brain areas, as a result of a more homogeneous magnetic field [Puts and Edden, 2012]. However, this also implies that pragmatic reasons rather than hypothesis‐driven arguments (e.g., based on postmortem studies examining GAD67 mRNA levels or neuroimaging studies) may have been decisive in the selection of brain region. Fortunately, a hypothesis‐driven approach is increasingly common as illustrated by recent PTSD studies in this meta‐analysis which focused on prefrontal‐limbic structures that have been implicated in the etiology of this disorder [Koenigs and Grafman, 2009]. Moreover, some promising technical advances have been made to increase the 1H‐MRS signal‐to‐noise ratio [Boer et al., 2015] and it may eventually be possible to map GABA levels across the brain with a high spectral resolution.

Probably the greatest challenge in deriving a reliable GABA signal from 1H‐MRS measurements is the disentanglement of the GABA signal from the macromolecular signal [Mullins et al., 2014]. Editing techniques are essential for filtering out relatively large overlapping signals from both creatine and macromolecules [Rothman et al., 1993]. However, even editing techniques cannot cancel out all contamination: the proportion of macromolecules in the GABA signal after editing has been estimated at almost 50% using MEGA‐PRESS at 3T [Aufhaus et al., 2013]. It is also clear that the methods for reducing the macromolecular contamination of the GABA signal differ greatly across studies (for examples of strategies to deal with macromolecular contamination of the GABA signal, see Mullins et al. [2014]). As a result, the proportion of actual GABA in the signal differs across studies and its variance will also not be homogeneous. Moreover, it is unclear whether differences in macromolecule concentration exist between patients with specific psychiatric disorders and controls, although there is no reason to assume such difference. Unfortunately, there is currently no consensus on the method to minimize the macromolecular contribution to the GABA signal.

In addition to regional differences and macromolecule contamination, other methodological factors that may have affected quantification of the 1H‐MRS GABA signal in this meta‐analysis are: (i) tissue composition (i.e., the amount of gray matter, white matter and cerebrospinal fluid); (ii) whether GABA is reported as a ratio over creatine or water; (iii) the specific software used for GABA quantification; (iv) the scanner and head coil type; (v) pulse sequence acquisition parameters; and vi) the specific rules for quality control of the acquired spectra (e.g., using linewidth as an indicator for the quality of the shimming procedure) and of the fitting (e.g., evaluating the Cramér‐Rao lower bound). The importance of these methodological issues cannot be underestimated since they may result in an increased variability in GABA levels across 1H‐MRS studies. In summary, obtaining accurate in vivo GABA levels remains a challenge.

Future Directions

Although efforts have been made to provide guidelines for minimal best practice for MEGA‐PRESS at 3T regarding acquisition, processing and analysis framework [Mullins et al., 2014], overall consensus in the GABA 1H‐MRS field remains an important goal. In addition, evidence for disease‐related changes in GABA 1H‐MRS levels would greatly benefit from longitudinal studies that provide more information about disease course and stability of the GABA signal over time. Importantly, longitudinal studies are unaffected by many confounders such as genetically determined differences in GABA levels [Berrettini et al., 1982; Luykx et al., 2012b]. There is also a need for studies with larger and more detailed samples to obtain more robust results as well as to investigate possible confounders such as disease history and medication use. In this context, a more integrative approach taking neuroimmune, stress and epigenetic markers into account may be of particular interest. Finally, increased spatial resolution of the 1H‐MRS signal may specifically improve our understanding of how regional brain GABA levels relate to psychopathology. With new developments to suppress the lipid signal in the skull, whole brain MRSI (magnetic resonance spectroscopic imaging) is possible within much shorter acquisition times [Boer et al., 2015]. This would enable MRSI with GABA‐editing, given a homogeneous magnetic field throughout the brain.

CONCLUSIONS

The present 1H‐MRS meta‐analysis shows that in vivo GABA levels are lower in depressed MDD patients and in ASD patients compared with controls. These results substantiate the importance of GABA in the etiology of both developmental disorders and disorders with a greater environmental component. The evidence suggests that the GABA system remains a promising target for pharmacological interventions in MDD and ASD. However, future studies could benefit from increased fundamental knowledge of the physiological variation in GABA levels, longitudinal studies, and consensus on the preferred methodology and minimal standards of human 1H‐MRS studies. Beyond these improvements lies the promise that detailed and accurate brain GABA level measures may advance diagnostic precision and improve personalized medicine.

Supporting information

Supporting Information

Click here for additional data file. (1.8MB, doc)

Funders had no role in design and reporting of the study. All authors reported no biomedical financial interests or potential conflicts of interest.

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